Industrial Alarm Management using Reinforcement Learning

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Abnormal situations cost industry millions of dollars every year. Alarm systems play important roles inensuring safe and efficient operation of modern industrial plants. However, most existing industrial alarmsystems suffer from alarm overloading. This implies that operators have to deal with too many alarms thatexceed the operators' capacity. Typically, plant operators receive much more alarms than they can handle.There has been an increasing interest in industry to address this issue and seek remedies to reduce the numberof false and nuisance alarms. Moreover, the current alarm messages do not indicate the root causes of thealarms or how to solve the alarms. An effective root cause analysis is highly desired.NTWIST Inc. provides advanced real-time alarm management solutions to process industries. Their goal is toeliminate preventable valve closures, unplanned equipment shutdowns and unplanned facilityoutages/shutdowns caused by alarm flooding and alarm suppression. Currently, NTWIST is developing areal-time alarm management system based on artificial intelligence. In this project, NTWIST works togetherwith Dr. Liu's group in the University of Alberta to learn from historical alarm data using reinforcementlearning and deep learning to address the issues with alarm flooding based on each alarm's impact on theprocess and safety conditions.